diff --git a/GPT_SoVITS/AR/models/t2s_model.py b/GPT_SoVITS/AR/models/t2s_model.py
index e140b4f..ed46b2b 100644
--- a/GPT_SoVITS/AR/models/t2s_model.py
+++ b/GPT_SoVITS/AR/models/t2s_model.py
@@ -97,7 +97,7 @@ class T2SBlock:
         k = k_cache.view(batch_size, kv_len, self.num_heads, -1).transpose(1, 2)
         v = v_cache.view(batch_size, kv_len, self.num_heads, -1).transpose(1, 2)
 
-        attn = F.scaled_dot_product_attention(q, k, v, ~attn_mask)
+        attn = F.scaled_dot_product_attention(q, k, v, attn_mask)
 
         attn = attn.permute(2, 0, 1, 3).reshape(batch_size*q_len, self.hidden_dim)
         attn = attn.view(q_len, batch_size, self.hidden_dim).transpose(1, 0)
@@ -532,6 +532,20 @@ class Text2SemanticDecoder(nn.Module):
             y = torch.zeros(x.shape[0], 0, dtype=torch.int, device=x.device)
             ref_free = True
 
+
+        ##### create mask #####
+        bsz = x.shape[0]
+        src_len = x_len + y_len
+        y_lens = torch.LongTensor([y_len]*bsz).to(x.device)
+        y_mask = make_pad_mask(y_lens)
+        x_mask = make_pad_mask(x_lens)
+
+        
+        xy_padding_mask = torch.concat([x_mask, y_mask], dim=1)
+        _xy_padding_mask = (
+            xy_padding_mask.view(bsz, 1, 1, src_len).expand(-1, self.num_head, -1, -1)
+        )
+
         x_attn_mask_pad = F.pad(
             x_attn_mask,
             (0, y_len),  ###xx的纯0扩展到xx纯0+xy纯1，(x,x+y)
@@ -545,7 +559,12 @@ class Text2SemanticDecoder(nn.Module):
         xy_attn_mask = torch.concat([x_attn_mask_pad, y_attn_mask], dim=0).to(
             x.device
         )
+        xy_attn_mask = xy_attn_mask.logical_or(_xy_padding_mask)
+        new_attn_mask = torch.zeros_like(xy_attn_mask, dtype=x.dtype)
+        new_attn_mask.masked_fill_(xy_attn_mask, float("-inf"))
+        xy_attn_mask = new_attn_mask
         
+        ###### decode #####
         y_list = [None]*y.shape[0]
         batch_idx_map = list(range(y.shape[0]))
         idx_list = [None]*y.shape[0]
diff --git a/GPT_SoVITS/TTS_infer_pack/TTS.py b/GPT_SoVITS/TTS_infer_pack/TTS.py
index cc460b8..ba29a03 100644
--- a/GPT_SoVITS/TTS_infer_pack/TTS.py
+++ b/GPT_SoVITS/TTS_infer_pack/TTS.py
@@ -361,6 +361,7 @@ class TTS:
             phones_list = []
             # bert_features_list = []
             all_phones_list = []
+            all_phones_len_list = []
             all_bert_features_list = []
             norm_text_batch = []
             bert_max_len = 0
@@ -376,16 +377,18 @@ class TTS:
                     phones = torch.LongTensor(item["phones"])
                     all_phones = phones.clone()
                     # norm_text = item["norm_text"]
+
                 bert_max_len = max(bert_max_len, all_bert_features.shape[-1])
                 phones_max_len = max(phones_max_len, phones.shape[-1])
                 
                 phones_list.append(phones)
                 all_phones_list.append(all_phones)
+                all_phones_len_list.append(all_phones.shape[-1])
                 all_bert_features_list.append(all_bert_features)
                 norm_text_batch.append(item["norm_text"])
-            # phones_batch = phones_list
+            phones_batch = phones_list
             max_len = max(bert_max_len, phones_max_len)
-            phones_batch = self.batch_sequences(phones_list, axis=0, pad_value=0, max_length=max_len)
+            # phones_batch = self.batch_sequences(phones_list, axis=0, pad_value=0, max_length=max_len)
             all_phones_batch = self.batch_sequences(all_phones_list, axis=0, pad_value=0, max_length=max_len)
             all_bert_features_batch = torch.FloatTensor(len(item_list), 1024, max_len)
             all_bert_features_batch.zero_()
@@ -397,6 +400,7 @@ class TTS:
             batch = {
                 "phones": phones_batch,
                 "all_phones": all_phones_batch,
+                "all_phones_len": torch.LongTensor(all_phones_len_list),
                 "all_bert_features": all_bert_features_batch,
                 "norm_text": norm_text_batch
             }
@@ -541,10 +545,12 @@ class TTS:
             t3 = ttime()
             batch_phones = item["phones"]
             all_phoneme_ids = item["all_phones"]
+            all_phoneme_lens = item["all_phones_len"]
             all_bert_features = item["all_bert_features"]
             norm_text = item["norm_text"]
             
             all_phoneme_ids = all_phoneme_ids.to(self.configs.device)
+            all_phoneme_lens = all_phoneme_lens.to(self.configs.device)
             all_bert_features = all_bert_features.to(self.configs.device)
             if self.configs.is_half:
                 all_bert_features = all_bert_features.half()
@@ -558,7 +564,7 @@ class TTS:
             with torch.no_grad():
                 pred_semantic_list, idx_list = self.t2s_model.model.infer_panel(
                     all_phoneme_ids,
-                    None,
+                    all_phoneme_lens,
                     prompt,
                     all_bert_features,
                     # prompt_phone_len=ph_offset,
@@ -588,7 +594,7 @@ class TTS:
             ## 改成串行处理
             batch_audio_fragment = []
             for i, idx in enumerate(idx_list):
-                phones = batch_phones[i].clone().unsqueeze(0).to(self.configs.device)
+                phones = batch_phones[i].unsqueeze(0).to(self.configs.device)
                 _pred_semantic = (pred_semantic_list[i][-idx:].unsqueeze(0).unsqueeze(0))   # .unsqueeze(0)#mq要多unsqueeze一次
                 audio_fragment =(self.vits_model.decode(
                         _pred_semantic, phones, refer_audio_spepc